Land Cover and Land Use (LCLU) segmentation plays a fundamental role in various remote sensing applications, including environmental monitoring, urban planning, and disaster management. Traditional models often face limitations in real-time processing and deployment on resource-constrained devices due to their high computational requirements. This paper presents a lightweight neural network designed to address these challenges by integrating dense dilated convolutions with pyramid depthwise convolutions for multiscale feature extraction. The proposed encoder-decoder architecture utilizes dense connections to aggregate spatial and contextual information across different resolutions, enhancing segmentation accuracy while minimizing computational overhead. The model's performance was rigorously evaluated using the NITRDrone and UDD6 datasets, demonstrating a segmentation accuracy of 94.8%, with a significantly reduced parameter count compared to state-of-the-art methods. The compact design of the network facilitates its implementation on low-power devices, enabling real-time LCLU analysis across diverse environmental conditions. This work underscores the potential of lightweight neural networks to advance remote sensing image processing, offering scalable and efficient solutions for practical applications in geospatial analysis.

Applied Science Research Center Applied Science Private University Amman 11931 Jordan

Center for Scientific Research and Entrepreneurship Northern Border University 73213 Arar Saudi Arabia

Computer Science and Artificial Intelligence Department College of Computing and Information Technology University of Bisha 14174 Bisha Saudi Arabia

Department of Computer Science Al Baha University 65779 Al Baha Saudi Arabia

Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P O Box 84428 11671 Riyadh Saudi Arabia

ENET Centre CEET VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

Jadara University Research Center Jadara University Irbid Jordan

National Engineering School of Gabes University of Gabes 6072 Gabes Tunisia

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